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Deploy Jupyter + TensorFlow
in one click.

Notebooks with TensorFlow and Keras for machine learning on the CPU, on a server we run for you.

Launch Jupyter + TensorFlow Browse all 44
Recommended from 8 GB RAM, from ~$0.03/hr (EU) 6 server locations No separate app fee

+$5.00 added at signup ยท exact template preserved ยท no card

What launches

The working stack,
preconfigured.

Choose the template and a project server. Selfhost.dev provisions the host and launches the application stack as one resource.

  • JupyterLab
  • TensorFlow and Keras
  • numpy, pandas and scikit-learn
  • Persistent home folder

Good fit for

Use it where
it does real work.

  • 01 Machine learning notebooks
  • 02 Keras model prototyping
  • 03 Teaching machine learning

From choice to app

A short path to
a usable deployment.

  1. 01

    Choose Jupyter + TensorFlow

    The page opens the exact console template. Pick a server size and one of six locations, then submit.

  2. 02

    Set access

    There is no username. Paste the password you set at deploy time into the Password or token box on the sign-in page.

  3. 03

    Let it start

    The console tracks provisioning, gives the app a generated HTTPS address and shows host CPU and memory after launch.

  4. 04

    Make it yours

    Paste your password into the Password or token box. Start a notebook. TensorFlow and Keras are installed beside the scientific Python stack and run on the CPU.

Know before deploy

The operational details
that matter.

This is JupyterLab with TensorFlow and Keras added to the scientific Python stack, so the image is larger and the first boot takes longer. Project servers have no GPU, so TensorFlow runs on the CPU. Your home folder persists across restarts, including notebooks, saved models and packages you add with pip install --user.

Capacity
Recommended from 8 GB RAM, from ~$0.03/hr (EU)
Topology
One project server
Initial address
Generated HTTPS URL
Protection
Optional whole-VM backup

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Frequently asked questions

Is Jupyter + TensorFlow available as a one-click template?
Yes. Jupyter + TensorFlow is one of 44 configurations in the console's global template chooser. The marketing page links directly to that configuration.
How much does it cost to run Jupyter + TensorFlow?
Recommended from 8 GB RAM, from ~$0.03/hr (EU). The app adds no separate fee. You pay only for the project server it runs on, by the hour against prepaid credits.
Does Jupyter + TensorFlow use a GPU?
No. Project servers have no GPU, so this template uses the CPU-only image and every model trains on the processor. Size the server for the data and models you plan to load.
How is Jupyter + TensorFlow different from JupyterLab?
It is the same JupyterLab with TensorFlow and Keras added to the scientific Python stack. The image is larger, so the console budgets 20 minutes for the first boot against 15 for JupyterLab.
How do I sign in to Jupyter + TensorFlow?
There is no username. Paste the password you set at deploy time into the Password or token box on the sign-in page.
Can I use my own domain for Jupyter + TensorFlow?
The app first receives a generated HTTPS address. After it is running, add one customer-owned domain from the resource Settings screen and follow the DNS instructions.
What does Selfhost.dev manage for this deployment?
Selfhost.dev provisions and maintains the project server, starts the Jupyter + TensorFlow template and shows server CPU and memory metrics. The application and its data run on that single server; optional backups protect the whole project VM.

Exact template selected

Put Jupyter + TensorFlow
on your own server.

The app adds no separate fee. You pay only for the project server it runs on, by the hour against prepaid credits. The console keeps this template selected through sign-up and onboarding.

Launch Jupyter + TensorFlow